Machine learning with oversampling for space debris classification based on radar cross section

Abstract

Over the past few years, the likelihood of collision of space objects increases as the quantity of space debris rises. Space debris classification and identification becomes more crucial to space assets security and space situation awareness. Radar cross section (RCS), one of the essential arguments for tracking space debris, was measured by European Incoherent Scatter Scientific Association (EISCAT) and other radar systems. This study investigates the effectiveness of seven machine learning methods employed to address the classification of space objects based on RCS data from European Space Agency (ESA). To tackle the Class-imbalance issue in this study (the ratio of space debris to non-debris is approximately 5:1 in the dataset), three oversampling techniques are employed, including: Synthetic Minority Oversampling Technique (SMOTE), synthetic minority oversampling technique-support vector machine (SMOTE-SVM) and Adaptive Synthetic Sampling (ADASYN). The experiments show that, in the test set, the combination of SVM with SMOTE-SVM oversampling techniques can reach the accuracy of 99.7%, the precision of 98.7% and the recall of 99.4% which is better than the rest of models.